{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "36cf6c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "from funcs import *\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] \n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "期货前缀ss = get_期货前缀ss(False)\n",
    "df_所有合约 = get_df_所有合约()\n",
    "code_delist_date_dict = get_code_delist_date_dict(df_所有合约)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "1a40dea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "008278f5eb5b4374be45754f7fa3e6b7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/77 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddds = []\n",
    "for 期货前缀 in tqdm(期货前缀ss):\n",
    "    ddd = get_ddd(期货前缀,code_delist_date_dict)\n",
    "    ddds.append(ddd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "c4d42aa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "s_date = '2010-01-01'\n",
    "e_date = '2023-12-30'\n",
    "\n",
    "cols = ['交易额0','交易额1',\n",
    "        '收益率0','收益率1',\n",
    "        '展期率01','展期率02',\n",
    "        '基差率01','基差率02',\n",
    "        '基差RSI','基差均值',\n",
    "        '跳开0','跳开1',\n",
    "        '持仓0','持仓1']\n",
    "\n",
    "for col in cols:\n",
    "    globals()[col] = pd.concat([x[col] for x in ddds],axis=1,keys=期货前缀ss)[s_date:e_date]\n",
    "    \n",
    "收益率m = (收益率0 + 收益率1)/2\n",
    "展期率01变化 = 展期率01.diff()\n",
    "展期率02变化 = 展期率02.diff()\n",
    "基差率01变化 = 基差率01.diff()\n",
    "基差率02变化 = 基差率02.diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "743c8385",
   "metadata": {},
   "outputs": [],
   "source": [
    "tp = 'M'\n",
    "tp1 = 'Q-JAN'\n",
    "tp2 = 'Q-FEB'\n",
    "tp3 = 'Q-MAR'\n",
    "\n",
    "con1 = 交易额0.rolling(120).mean() > 500000\n",
    "con2 = (交易额0+交易额1).rolling(120).mean().rank(1,pct=True) > 0.2\n",
    "交易额过滤条件 = con1 & con2\n",
    "过滤因子 = 交易额过滤条件.resample(tp).last()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ce0fde6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "2a588b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "def previous_year_month(input_string):\n",
    "    year = int(input_string[:2])\n",
    "    month = input_string[2:]\n",
    "    previous_year = year - 1\n",
    "    return f\"{previous_year:02d}{month}\"\n",
    "\n",
    "期货前缀 = 'sc'\n",
    "df = get_df(期货前缀)\n",
    "df_open = df.pivot(index='t', columns='code', values='open')\n",
    "df_high = df.pivot(index='t', columns='code', values='high')\n",
    "df_low = df.pivot(index='t', columns='code', values='low')\n",
    "df_close = df.pivot(index='t', columns='code', values='close')\n",
    "df_oi = df.pivot(index='t', columns='code', values='oi')\n",
    "df_money = df.pivot(index='t', columns='code', values='amount')\n",
    "当前代码0 = get_上m期排名第n的col(df_money,1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "5e3dce94",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3d46e7a0b3d46dd9f26e570c5713830",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/77 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xxs = []\n",
    "for 期货前缀 in tqdm(期货前缀ss):\n",
    "    df = get_df(期货前缀)\n",
    "    df_open = df.pivot(index='t', columns='code', values='open')\n",
    "    df_high = df.pivot(index='t', columns='code', values='high')\n",
    "    df_low = df.pivot(index='t', columns='code', values='low')\n",
    "    df_close = df.pivot(index='t', columns='code', values='close')\n",
    "    df_oi = df.pivot(index='t', columns='code', values='oi')\n",
    "    df_money = df.pivot(index='t', columns='code', values='amount')\n",
    "    当前代码0 = get_上m期排名第n的col(df_money,1,0)\n",
    "\n",
    "#     dd = (df_high.rolling(63).max() - df_low.rolling(63).min()).copy()\n",
    "    dd = df_close.rolling(126).mean().copy()\n",
    "    trade_dates = dd.index\n",
    "    dd = dd.resample('D').last().fillna(method='ffill')\n",
    "    dd2 = dd.copy()\n",
    "    dd2.index = dd.index +  pd.DateOffset(years=1)\n",
    "    dd2 = dd2.groupby(level=0).last()\n",
    "    dd_x = pd.DataFrame(np.nan,index=dd.index,columns=dd.columns)\n",
    "    for col in dd.columns:\n",
    "        previous_col = previous_year_month(col)\n",
    "        if previous_col in dd.columns:\n",
    "            #dd_x[col] = (dd[col] - dd2[previous_year_month(col)])/dd2[previous_year_month(col)]\n",
    "            dd_x[col] = dd[col] - dd2[previous_year_month(col)]\n",
    "\n",
    "    dd_x = dd_x[dd_x.index.isin(trade_dates)]\n",
    "    xxx1 = df_close.apply(lambda x: x.get(当前代码0.get(x.name)),axis=1)\n",
    "    xxx2 = dd_x.apply(lambda x: x.get(当前代码0.get(x.name)),axis=1)\n",
    "    xx = xxx2/xxx1\n",
    "    xxs.append(xx)\n",
    "dff = pd.concat(xxs,axis=1)\n",
    "dff.columns = 期货前缀ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "54a67563",
   "metadata": {},
   "outputs": [],
   "source": [
    "dffx = dff.resample(tp).last().where(过滤因子)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "39bc4f11",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "xxx = get_因子日度收益(dffx,'xx',收益率0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "a287b0d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27b064b9eb0>"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xxx.cumsum().plot(figsize=(14,6),grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "f5829654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4497356417394611"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(250**0.5)*xxx[:'2021-01-01'].mean()/xxx[:'2021-01-01'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daae0f35",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a31e56f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "27c2925a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xxx0 = df_close.pct_change().apply(lambda x: x.get(当前代码0.get(x.name)),axis=1)\n",
    "xxx1 = df_close.apply(lambda x: x.get(当前代码0.get(x.name)),axis=1)\n",
    "xxx2 = dd_x.apply(lambda x: x.get(当前代码0.get(x.name)),axis=1)\n",
    "xx = xxx2/xxx1\n",
    "\n",
    "fig = plt.figure(figsize=(14,6))\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.plot(xxx0.cumsum())\n",
    "ax1.grid()\n",
    "ax2 = ax1.twinx() \n",
    "ax2.plot(xx,'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "77f48996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1651.0"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "0af0d5c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2209'"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "当前代码0['2022-07-22']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "0e6641c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1406.0"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_close['2209']['2022-07-22']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "104cc52e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3057.0"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_close['2109']['2021-07-22']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "021182ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1406.0"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_close['2209']['2022-07-22']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "c70b9c16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2109'"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "当前代码0['2021-07-22']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66e660fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "cfe0ebec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1651.0"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1406.0 - 3057.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "0d1484ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1406.0"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_close['2209']['2022-07-22']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "865a6069",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc49cb63",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "e3b39ab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2023-01-17     7674.0\n",
       "2023-01-18    11082.0\n",
       "2023-01-19    10546.0\n",
       "2023-01-20    12201.0\n",
       "2023-01-30    14494.0\n",
       "               ...   \n",
       "2023-12-25    65671.0\n",
       "2023-12-26    48695.0\n",
       "2023-12-27    39328.0\n",
       "2023-12-28    28719.0\n",
       "2023-12-29     4006.0\n",
       "Name: 2401, Length: 232, dtype: float64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_oi['2401']['2023-01-01':].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "2e1370f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2022-01-18     113.0\n",
       "2022-01-19     411.0\n",
       "2022-01-20     519.0\n",
       "2022-01-21     608.0\n",
       "2022-01-24     805.0\n",
       "               ...  \n",
       "2023-01-10    3520.0\n",
       "2023-01-11    3506.0\n",
       "2023-01-12    3388.0\n",
       "2023-01-13    3395.0\n",
       "2023-01-16       0.0\n",
       "Name: 2301, Length: 242, dtype: float64"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_oi['2301']['2022-01-01':].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "937415f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.070796460177"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(11082 - 113)/113"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4a03913",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90e38085",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "3ef94910",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa1 = dd_x.index\n",
    "aa2 = ((dd2[col] - dd[previous_year_month(col)])/dd[previous_year_month(col)]).index\n",
    "\n",
    "set(aa1) - set(aa2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "dced64d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-02-28 00:00:00')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime('2020-02-29') - pd.DateOffset(years=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "6d602332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>code</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2101</th>\n",
       "      <th>2102</th>\n",
       "      <th>...</th>\n",
       "      <th>2403</th>\n",
       "      <th>2404</th>\n",
       "      <th>2405</th>\n",
       "      <th>2406</th>\n",
       "      <th>2407</th>\n",
       "      <th>2408</th>\n",
       "      <th>2409</th>\n",
       "      <th>2410</th>\n",
       "      <th>2411</th>\n",
       "      <th>2412</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-06</th>\n",
       "      <td>1561.0</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1587.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-07</th>\n",
       "      <td>1561.0</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1587.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-08</th>\n",
       "      <td>1561.0</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1587.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-09</th>\n",
       "      <td>1572.0</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1604.0</td>\n",
       "      <td>1587.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-10</th>\n",
       "      <td>1634.0</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>1654.0</td>\n",
       "      <td>1641.0</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-25</th>\n",
       "      <td>1150.0</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2210.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>2076.0</td>\n",
       "      <td>2053.0</td>\n",
       "      <td>2054.0</td>\n",
       "      <td>2033.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-26</th>\n",
       "      <td>1150.0</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2207.0</td>\n",
       "      <td>2151.0</td>\n",
       "      <td>2101.0</td>\n",
       "      <td>2068.0</td>\n",
       "      <td>2042.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>1988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>1150.0</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2166.0</td>\n",
       "      <td>2106.0</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>1966.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>1150.0</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>2083.0</td>\n",
       "      <td>2055.0</td>\n",
       "      <td>2028.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1953.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>1150.0</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2104.0</td>\n",
       "      <td>2061.0</td>\n",
       "      <td>2044.0</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1484 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "code          2005    2006    2007    2008    2009    2010    2011    2012  \\\n",
       "t                                                                            \n",
       "2018-12-06  1561.0  1572.0  1581.0  1581.0  1584.0  1578.0  1587.0     NaN   \n",
       "2018-12-07  1561.0  1572.0  1581.0  1581.0  1584.0  1578.0  1587.0     NaN   \n",
       "2018-12-08  1561.0  1572.0  1581.0  1581.0  1584.0  1578.0  1587.0     NaN   \n",
       "2018-12-09  1572.0  1572.0  1578.0  1604.0  1587.0  1578.0  1594.0     NaN   \n",
       "2018-12-10  1634.0  1572.0  1578.0  1648.0  1654.0  1641.0  1635.0     NaN   \n",
       "...            ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "2022-12-25  1150.0  1223.0  1125.0  1442.0  1830.0  1660.0  1470.0  1395.0   \n",
       "2022-12-26  1150.0  1223.0  1125.0  1442.0  1830.0  1660.0  1470.0  1395.0   \n",
       "2022-12-27  1150.0  1223.0  1125.0  1442.0  1830.0  1660.0  1470.0  1395.0   \n",
       "2022-12-28  1150.0  1223.0  1125.0  1442.0  1830.0  1660.0  1470.0  1395.0   \n",
       "2022-12-29  1150.0  1223.0  1125.0  1442.0  1830.0  1660.0  1470.0  1395.0   \n",
       "\n",
       "code          2101    2102  ...    2403    2404    2405    2406    2407  \\\n",
       "t                           ...                                           \n",
       "2018-12-06     NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "2018-12-07     NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "2018-12-08     NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "2018-12-09     NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "2018-12-10     NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "...            ...     ...  ...     ...     ...     ...     ...     ...   \n",
       "2022-12-25  1338.0  1490.0  ...  2210.0  2155.0  2110.0  2076.0  2053.0   \n",
       "2022-12-26  1338.0  1490.0  ...  2207.0  2151.0  2101.0  2068.0  2042.0   \n",
       "2022-12-27  1338.0  1490.0  ...  2166.0  2106.0  2079.0  2049.0  2023.0   \n",
       "2022-12-28  1338.0  1490.0  ...  2136.0  2083.0  2055.0  2028.0  2000.0   \n",
       "2022-12-29  1338.0  1490.0  ...  2104.0  2061.0  2044.0  2023.0  1992.0   \n",
       "\n",
       "code          2408    2409    2410    2411    2412  \n",
       "t                                                   \n",
       "2018-12-06     NaN     NaN     NaN     NaN     NaN  \n",
       "2018-12-07     NaN     NaN     NaN     NaN     NaN  \n",
       "2018-12-08     NaN     NaN     NaN     NaN     NaN  \n",
       "2018-12-09     NaN     NaN     NaN     NaN     NaN  \n",
       "2018-12-10     NaN     NaN     NaN     NaN     NaN  \n",
       "...            ...     ...     ...     ...     ...  \n",
       "2022-12-25  2054.0  2033.0  2018.0  1996.0  1996.0  \n",
       "2022-12-26  2041.0  2019.0  2008.0  1985.0  1988.0  \n",
       "2022-12-27  2015.0  1995.0  1984.0  1962.0  1966.0  \n",
       "2022-12-28  1999.0  1980.0  1969.0  1950.0  1953.0  \n",
       "2022-12-29  1975.0  1962.0  1956.0  1938.0  1930.0  \n",
       "\n",
       "[1484 rows x 56 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd2.groupby(level=0).last()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67818b46",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "d232c985",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2018-12-06   NaN\n",
       "2018-12-07   NaN\n",
       "2018-12-08   NaN\n",
       "2018-12-09   NaN\n",
       "2018-12-10   NaN\n",
       "              ..\n",
       "2023-12-25   NaN\n",
       "2023-12-26   NaN\n",
       "2023-12-27   NaN\n",
       "2023-12-28   NaN\n",
       "2023-12-29   NaN\n",
       "Length: 1851, dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dd2[col] - dd[previous_year_month(col)])/dd[previous_year_month(col)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75a2b325",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "e48f173b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(dd2[col].index)) - len(dd2[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "4d4e446a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-02-28    2\n",
       "2018-12-21    1\n",
       "2022-10-03    1\n",
       "2020-04-09    1\n",
       "2020-12-04    1\n",
       "             ..\n",
       "2022-07-20    1\n",
       "2021-09-14    1\n",
       "2021-05-25    1\n",
       "2021-06-06    1\n",
       "2021-08-16    1\n",
       "Name: t, Length: 1484, dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(dd2[col].index).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cd93954",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "33a56ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-12-06', '2018-12-07', '2018-12-08', '2018-12-09',\n",
       "               '2018-12-10', '2018-12-11', '2018-12-12', '2018-12-13',\n",
       "               '2018-12-14', '2018-12-15',\n",
       "               ...\n",
       "               '2023-12-20', '2023-12-21', '2023-12-22', '2023-12-23',\n",
       "               '2023-12-24', '2023-12-25', '2023-12-26', '2023-12-27',\n",
       "               '2023-12-28', '2023-12-29'],\n",
       "              dtype='datetime64[ns]', name='t', length=1851, freq=None)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((dd2[col] - dd[previous_year_month(col)])/dd[previous_year_month(col)]).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "393df21b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reindex from a duplicate axis",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-94-9bbf406c0b3e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdd_x\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdd2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mdd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mprevious_year_month\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mdd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mprevious_year_month\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   3161\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3162\u001b[0m             \u001b[1;31m# set column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3163\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3165\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   3240\u001b[0m         \"\"\"\n\u001b[0;32m   3241\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3242\u001b[1;33m         \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3243\u001b[0m         \u001b[0mNDFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3244\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_sanitize_column\u001b[1;34m(self, key, value, broadcast)\u001b[0m\n\u001b[0;32m   3874\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3875\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3876\u001b[1;33m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreindexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3877\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3878\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mreindexer\u001b[1;34m(value)\u001b[0m\n\u001b[0;32m   3865\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3866\u001b[0m                         \u001b[1;31m# duplicate axis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3867\u001b[1;33m                         \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3868\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3869\u001b[0m                     \u001b[1;31m# other\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mreindexer\u001b[1;34m(value)\u001b[0m\n\u001b[0;32m   3860\u001b[0m                 \u001b[1;31m# GH 4107\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3861\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3862\u001b[1;33m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3863\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3864\u001b[0m                     \u001b[1;31m# raised in MultiIndex.from_tuples, see test_insert_error_msmgs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mreindex\u001b[1;34m(self, index, **kwargs)\u001b[0m\n\u001b[0;32m   4343\u001b[0m     )\n\u001b[0;32m   4344\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4345\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4346\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4347\u001b[0m     def drop(\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mreindex\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   4809\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4810\u001b[0m         \u001b[1;31m# perform the reindex on the axes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4811\u001b[1;33m         return self._reindex_axes(\n\u001b[0m\u001b[0;32m   4812\u001b[0m             \u001b[0maxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4813\u001b[0m         ).__finalize__(self, method=\"reindex\")\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_reindex_axes\u001b[1;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[0;32m   4830\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4831\u001b[0m             \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4832\u001b[1;33m             obj = obj._reindex_with_indexers(\n\u001b[0m\u001b[0;32m   4833\u001b[0m                 \u001b[1;33m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnew_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4834\u001b[0m                 \u001b[0mfill_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_reindex_with_indexers\u001b[1;34m(self, reindexers, fill_value, copy, allow_dups)\u001b[0m\n\u001b[0;32m   4875\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4876\u001b[0m             \u001b[1;31m# TODO: speed up on homogeneous DataFrame objects\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4877\u001b[1;33m             new_data = new_data.reindex_indexer(\n\u001b[0m\u001b[0;32m   4878\u001b[0m                 \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4879\u001b[0m                 \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mreindex_indexer\u001b[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate, only_slice)\u001b[0m\n\u001b[0;32m   1299\u001b[0m         \u001b[1;31m# some axes don't allow reindexing with dups\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1300\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1301\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_reindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1303\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_can_reindex\u001b[1;34m(self, indexer)\u001b[0m\n\u001b[0;32m   3474\u001b[0m         \u001b[1;31m# trying to reindex on an axis with duplicates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3475\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_index_as_unique\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3476\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"cannot reindex from a duplicate axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3477\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3478\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot reindex from a duplicate axis"
     ]
    }
   ],
   "source": [
    "dd_x[col] = (dd2[col] - dd[previous_year_month(col)])/dd[previous_year_month(col)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c29bf24d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34fc614a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0e6ac44c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>code</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2101</th>\n",
       "      <th>2102</th>\n",
       "      <th>...</th>\n",
       "      <th>2403</th>\n",
       "      <th>2404</th>\n",
       "      <th>2405</th>\n",
       "      <th>2406</th>\n",
       "      <th>2407</th>\n",
       "      <th>2408</th>\n",
       "      <th>2409</th>\n",
       "      <th>2410</th>\n",
       "      <th>2411</th>\n",
       "      <th>2412</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-06</th>\n",
       "      <td>1561.0</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1587.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-07</th>\n",
       "      <td>1561.0</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1587.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-08</th>\n",
       "      <td>1561.0</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1587.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-09</th>\n",
       "      <td>1572.0</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1604.0</td>\n",
       "      <td>1587.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-10</th>\n",
       "      <td>1634.0</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>1654.0</td>\n",
       "      <td>1641.0</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-25</th>\n",
       "      <td>1150.0</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2210.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>2076.0</td>\n",
       "      <td>2053.0</td>\n",
       "      <td>2054.0</td>\n",
       "      <td>2033.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-26</th>\n",
       "      <td>1150.0</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2207.0</td>\n",
       "      <td>2151.0</td>\n",
       "      <td>2101.0</td>\n",
       "      <td>2068.0</td>\n",
       "      <td>2042.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>1988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-27</th>\n",
       "      <td>1150.0</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2166.0</td>\n",
       "      <td>2106.0</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>1966.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-28</th>\n",
       "      <td>1150.0</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>2083.0</td>\n",
       "      <td>2055.0</td>\n",
       "      <td>2028.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1953.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-29</th>\n",
       "      <td>1150.0</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2104.0</td>\n",
       "      <td>2061.0</td>\n",
       "      <td>2044.0</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1485 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "code          2005    2006    2007    2008    2009    2010    2011    2012  \\\n",
       "t                                                                            \n",
       "2019-12-06  1561.0  1572.0  1581.0  1581.0  1584.0  1578.0  1587.0     NaN   \n",
       "2019-12-07  1561.0  1572.0  1581.0  1581.0  1584.0  1578.0  1587.0     NaN   \n",
       "2019-12-08  1561.0  1572.0  1581.0  1581.0  1584.0  1578.0  1587.0     NaN   \n",
       "2019-12-09  1572.0  1572.0  1578.0  1604.0  1587.0  1578.0  1594.0     NaN   \n",
       "2019-12-10  1634.0  1572.0  1578.0  1648.0  1654.0  1641.0  1635.0     NaN   \n",
       "...            ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "2023-12-25  1150.0  1223.0  1125.0  1442.0  1830.0  1660.0  1470.0  1395.0   \n",
       "2023-12-26  1150.0  1223.0  1125.0  1442.0  1830.0  1660.0  1470.0  1395.0   \n",
       "2023-12-27  1150.0  1223.0  1125.0  1442.0  1830.0  1660.0  1470.0  1395.0   \n",
       "2023-12-28  1150.0  1223.0  1125.0  1442.0  1830.0  1660.0  1470.0  1395.0   \n",
       "2023-12-29  1150.0  1223.0  1125.0  1442.0  1830.0  1660.0  1470.0  1395.0   \n",
       "\n",
       "code          2101    2102  ...    2403    2404    2405    2406    2407  \\\n",
       "t                           ...                                           \n",
       "2019-12-06     NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "2019-12-07     NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "2019-12-08     NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "2019-12-09     NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "2019-12-10     NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "...            ...     ...  ...     ...     ...     ...     ...     ...   \n",
       "2023-12-25  1338.0  1490.0  ...  2210.0  2155.0  2110.0  2076.0  2053.0   \n",
       "2023-12-26  1338.0  1490.0  ...  2207.0  2151.0  2101.0  2068.0  2042.0   \n",
       "2023-12-27  1338.0  1490.0  ...  2166.0  2106.0  2079.0  2049.0  2023.0   \n",
       "2023-12-28  1338.0  1490.0  ...  2136.0  2083.0  2055.0  2028.0  2000.0   \n",
       "2023-12-29  1338.0  1490.0  ...  2104.0  2061.0  2044.0  2023.0  1992.0   \n",
       "\n",
       "code          2408    2409    2410    2411    2412  \n",
       "t                                                   \n",
       "2019-12-06     NaN     NaN     NaN     NaN     NaN  \n",
       "2019-12-07     NaN     NaN     NaN     NaN     NaN  \n",
       "2019-12-08     NaN     NaN     NaN     NaN     NaN  \n",
       "2019-12-09     NaN     NaN     NaN     NaN     NaN  \n",
       "2019-12-10     NaN     NaN     NaN     NaN     NaN  \n",
       "...            ...     ...     ...     ...     ...  \n",
       "2023-12-25  2054.0  2033.0  2018.0  1996.0  1996.0  \n",
       "2023-12-26  2041.0  2019.0  2008.0  1985.0  1988.0  \n",
       "2023-12-27  2015.0  1995.0  1984.0  1962.0  1966.0  \n",
       "2023-12-28  1999.0  1980.0  1969.0  1950.0  1953.0  \n",
       "2023-12-29  1975.0  1962.0  1956.0  1938.0  1930.0  \n",
       "\n",
       "[1485 rows x 56 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.resample('D').last().fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "817c7480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "365.0"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1460/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7879e93",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de6bc489",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "82e1d086",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2019-12-06   NaN\n",
       "2019-12-09   NaN\n",
       "2019-12-10   NaN\n",
       "2019-12-11   NaN\n",
       "2019-12-12   NaN\n",
       "              ..\n",
       "2023-12-25   NaN\n",
       "2023-12-26   NaN\n",
       "2023-12-27   NaN\n",
       "2023-12-28   NaN\n",
       "2023-12-29   NaN\n",
       "Name: 2401, Length: 988, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_x['2401']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "aba4dd54",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2023-12-01    2554.0\n",
       "2023-12-04    2637.0\n",
       "2023-12-05    2776.0\n",
       "2023-12-06    2723.0\n",
       "2023-12-07    2730.0\n",
       "2023-12-08    2672.0\n",
       "2023-12-11    2672.0\n",
       "2023-12-12    2677.0\n",
       "2023-12-13    2657.0\n",
       "2023-12-14    2670.0\n",
       "2023-12-15    2667.0\n",
       "2023-12-18    2644.0\n",
       "2023-12-19    2523.0\n",
       "2023-12-20    2534.0\n",
       "2023-12-21    2573.0\n",
       "2023-12-22    2601.0\n",
       "2023-12-25    2545.0\n",
       "2023-12-26    2514.0\n",
       "2023-12-27    2501.0\n",
       "2023-12-28    2496.0\n",
       "2023-12-29    2500.0\n",
       "Name: 2401, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_close['2401']['2023-12-01':'2024-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c8648a84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2022-12-01    2718.0\n",
       "2022-12-02    2707.0\n",
       "2022-12-05    2662.0\n",
       "2022-12-06    2670.0\n",
       "2022-12-07    2673.0\n",
       "2022-12-08    2712.0\n",
       "2022-12-09    2777.0\n",
       "2022-12-12    2826.0\n",
       "2022-12-13    2810.0\n",
       "2022-12-14    2812.0\n",
       "2022-12-15    2866.0\n",
       "2022-12-16    2877.0\n",
       "2022-12-19    2837.0\n",
       "2022-12-20    2854.0\n",
       "2022-12-21    2868.0\n",
       "2022-12-22    2819.0\n",
       "2022-12-23    2795.0\n",
       "2022-12-26    2837.0\n",
       "2022-12-27    2859.0\n",
       "2022-12-28    2861.0\n",
       "2022-12-29    2818.0\n",
       "2022-12-30    2931.0\n",
       "Name: 2301, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_close['2301']['2022-12-01':'2023-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a90cb918",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b90ee71",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2465f872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: 2401, dtype: float64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_close.copy()\n",
    "# Function to calculate the percentage change between contracts\n",
    "def calculate_percentage_change(current_year_contract, previous_year_contract):\n",
    "    if previous_year_contract in df.columns:\n",
    "        return (df[current_year_contract] - df[previous_year_contract]) / df[previous_year_contract] * 100\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Calculate the percentage change for each contract\n",
    "percentage_changes = {}\n",
    "for contract in df.columns:\n",
    "    # Extract the year and month from the contract name\n",
    "    year, month = int(contract[:2]), int(contract[2:])\n",
    "    # Calculate the previous year's contract name\n",
    "    previous_year_contract = f\"{year - 1:02d}{month:02d}\"\n",
    "    # Calculate the percentage change and store it\n",
    "    percentage_changes[contract] = calculate_percentage_change(contract, previous_year_contract)\n",
    "\n",
    "# Convert the dictionary to a DataFrame\n",
    "percentage_change_df = pd.DataFrame(percentage_changes)\n",
    "\n",
    "percentage_change_df['2401'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dfaf66d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac5f7591",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "567514ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "43db0653",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>code</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2101</th>\n",
       "      <th>2102</th>\n",
       "      <th>...</th>\n",
       "      <th>2403</th>\n",
       "      <th>2404</th>\n",
       "      <th>2405</th>\n",
       "      <th>2406</th>\n",
       "      <th>2407</th>\n",
       "      <th>2408</th>\n",
       "      <th>2409</th>\n",
       "      <th>2410</th>\n",
       "      <th>2411</th>\n",
       "      <th>2412</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>988 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "code        2005  2006  2007  2008  2009  2010  2011  2012  2101  2102  ...  \\\n",
       "t                                                                       ...   \n",
       "2019-12-06   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "2019-12-09   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "2019-12-10   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "2019-12-11   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "2019-12-12   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "...          ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
       "2023-12-25   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "2023-12-26   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "2023-12-27   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "2023-12-28   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "2023-12-29   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "\n",
       "code        2403  2404  2405  2406  2407  2408  2409  2410  2411  2412  \n",
       "t                                                                       \n",
       "2019-12-06   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2019-12-09   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2019-12-10   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2019-12-11   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2019-12-12   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "...          ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "2023-12-25   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2023-12-26   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2023-12-27   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2023-12-28   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2023-12-29   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[988 rows x 56 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "13cff4e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: 2301, dtype: float64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_x['2301'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e4d516e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "8eee1f07",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x209ed516e20>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_previous_year_data(df, column, start_year_format=\"%y\"):\n",
    "    year = int(column[:2])\n",
    "    month = column[2:]\n",
    "\n",
    "    start_date = f\"{year-1:02d}{month}-01\"\n",
    "    end_date = f\"{year:02d}{month}-01\"\n",
    "    start_date = pd.to_datetime(start_date, format=start_year_format + \"%m-%d\")\n",
    "    end_date = pd.to_datetime(end_date, format=start_year_format + \"%m-%d\")\n",
    "    data = df[column][start_date:end_date]\n",
    "    data.index = data.index.map(lambda x: x.replace(year=2024))\n",
    "    return data\n",
    "\n",
    "xx1 = get_previous_year_data(df_oi, '2005')\n",
    "xx2 = get_previous_year_data(df_oi, '2105')\n",
    "xx3 = get_previous_year_data(df_oi, '2205')\n",
    "xx4 = get_previous_year_data(df_oi, '2305')\n",
    "xx5 = get_previous_year_data(df_oi, '2405')\n",
    "xx1.plot(figsize=(14,6),grid=True,legend=True)\n",
    "xx2.plot(figsize=(14,6),grid=True,legend=True)\n",
    "xx3.plot(figsize=(14,6),grid=True,legend=True)\n",
    "xx4.plot(figsize=(14,6),grid=True,legend=True)\n",
    "xx5.plot(figsize=(14,6),grid=True,legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "a3ec0dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>code</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2101</th>\n",
       "      <th>2102</th>\n",
       "      <th>...</th>\n",
       "      <th>2403</th>\n",
       "      <th>2404</th>\n",
       "      <th>2405</th>\n",
       "      <th>2406</th>\n",
       "      <th>2407</th>\n",
       "      <th>2408</th>\n",
       "      <th>2409</th>\n",
       "      <th>2410</th>\n",
       "      <th>2411</th>\n",
       "      <th>2412</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-06</th>\n",
       "      <td>38922.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-09</th>\n",
       "      <td>31568.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6160.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-10</th>\n",
       "      <td>68176.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8272.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-11</th>\n",
       "      <td>66020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6372.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-12</th>\n",
       "      <td>70048.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5936.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>24574.0</td>\n",
       "      <td>7133.0</td>\n",
       "      <td>457605.0</td>\n",
       "      <td>7834.0</td>\n",
       "      <td>7292.0</td>\n",
       "      <td>12990.0</td>\n",
       "      <td>91837.0</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>4007.0</td>\n",
       "      <td>766.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>24216.0</td>\n",
       "      <td>7161.0</td>\n",
       "      <td>461253.0</td>\n",
       "      <td>7761.0</td>\n",
       "      <td>7322.0</td>\n",
       "      <td>12894.0</td>\n",
       "      <td>89261.0</td>\n",
       "      <td>3273.0</td>\n",
       "      <td>4108.0</td>\n",
       "      <td>789.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25653.0</td>\n",
       "      <td>8999.0</td>\n",
       "      <td>477115.0</td>\n",
       "      <td>7633.0</td>\n",
       "      <td>7535.0</td>\n",
       "      <td>12750.0</td>\n",
       "      <td>92945.0</td>\n",
       "      <td>3214.0</td>\n",
       "      <td>4183.0</td>\n",
       "      <td>875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25547.0</td>\n",
       "      <td>11201.0</td>\n",
       "      <td>468558.0</td>\n",
       "      <td>8107.0</td>\n",
       "      <td>7272.0</td>\n",
       "      <td>12309.0</td>\n",
       "      <td>92402.0</td>\n",
       "      <td>3211.0</td>\n",
       "      <td>4329.0</td>\n",
       "      <td>930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>26049.0</td>\n",
       "      <td>11770.0</td>\n",
       "      <td>449616.0</td>\n",
       "      <td>8106.0</td>\n",
       "      <td>7193.0</td>\n",
       "      <td>12197.0</td>\n",
       "      <td>95033.0</td>\n",
       "      <td>3185.0</td>\n",
       "      <td>4299.0</td>\n",
       "      <td>983.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>988 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "code           2005  2006  2007  2008    2009  2010  2011  2012  2101  2102  \\\n",
       "t                                                                             \n",
       "2019-12-06  38922.0   2.0   4.0   4.0  6054.0  36.0  22.0   NaN   NaN   NaN   \n",
       "2019-12-09  31568.0   NaN   2.0   6.0  6160.0  36.0  22.0   NaN   NaN   NaN   \n",
       "2019-12-10  68176.0   NaN   NaN   6.0  8272.0  10.0  46.0   NaN   NaN   NaN   \n",
       "2019-12-11  66020.0   NaN   NaN   NaN  6372.0   8.0  38.0   NaN   NaN   NaN   \n",
       "2019-12-12  70048.0   4.0   NaN   NaN  5936.0   8.0   NaN   NaN   NaN   NaN   \n",
       "...             ...   ...   ...   ...     ...   ...   ...   ...   ...   ...   \n",
       "2023-12-25      NaN   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2023-12-26      NaN   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2023-12-27      NaN   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2023-12-28      NaN   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2023-12-29      NaN   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "code        ...     2403     2404      2405    2406    2407     2408     2409  \\\n",
       "t           ...                                                                 \n",
       "2019-12-06  ...      NaN      NaN       NaN     NaN     NaN      NaN      NaN   \n",
       "2019-12-09  ...      NaN      NaN       NaN     NaN     NaN      NaN      NaN   \n",
       "2019-12-10  ...      NaN      NaN       NaN     NaN     NaN      NaN      NaN   \n",
       "2019-12-11  ...      NaN      NaN       NaN     NaN     NaN      NaN      NaN   \n",
       "2019-12-12  ...      NaN      NaN       NaN     NaN     NaN      NaN      NaN   \n",
       "...         ...      ...      ...       ...     ...     ...      ...      ...   \n",
       "2023-12-25  ...  24574.0   7133.0  457605.0  7834.0  7292.0  12990.0  91837.0   \n",
       "2023-12-26  ...  24216.0   7161.0  461253.0  7761.0  7322.0  12894.0  89261.0   \n",
       "2023-12-27  ...  25653.0   8999.0  477115.0  7633.0  7535.0  12750.0  92945.0   \n",
       "2023-12-28  ...  25547.0  11201.0  468558.0  8107.0  7272.0  12309.0  92402.0   \n",
       "2023-12-29  ...  26049.0  11770.0  449616.0  8106.0  7193.0  12197.0  95033.0   \n",
       "\n",
       "code          2410    2411   2412  \n",
       "t                                  \n",
       "2019-12-06     NaN     NaN    NaN  \n",
       "2019-12-09     NaN     NaN    NaN  \n",
       "2019-12-10     NaN     NaN    NaN  \n",
       "2019-12-11     NaN     NaN    NaN  \n",
       "2019-12-12     NaN     NaN    NaN  \n",
       "...            ...     ...    ...  \n",
       "2023-12-25  3212.0  4007.0  766.0  \n",
       "2023-12-26  3273.0  4108.0  789.0  \n",
       "2023-12-27  3214.0  4183.0  875.0  \n",
       "2023-12-28  3211.0  4329.0  930.0  \n",
       "2023-12-29  3185.0  4299.0  983.0  \n",
       "\n",
       "[988 rows x 56 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_oi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f61ee821",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e223c2b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2816a918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1692.87"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "12.4+7.08+34.71+13+292.82+12.4+34.71+13+7.08+302.8+\\\n",
    "9.3+460.95+40.05+8.26+15+70.26+15.5+89.49+15.5+40.05+\\\n",
    "8.26+15+70.26+15.5+89.49"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "a6917257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: 2405, dtype: float64)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx5.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9c18e456",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot construct a Timedelta from the passed arguments, allowed keywords are [weeks, days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mpandas\\_libs\\tslibs\\timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.Timedelta.__new__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'years' is an invalid keyword argument for __new__()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-84-97d3e3577e49>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mxx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_oi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'2205'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'2021-05-01'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'2022-05-01'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mxx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTimedelta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myears\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mpandas\\_libs\\tslibs\\timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.Timedelta.__new__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot construct a Timedelta from the passed arguments, allowed keywords are [weeks, days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds]"
     ]
    }
   ],
   "source": [
    "xx = df_oi['2205']['2021-05-01':'2022-05-01']\n",
    "xx.index = xx.index - pd.Timedelta(years=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c51776c9",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot construct a Timedelta from the passed arguments, allowed keywords are [weeks, days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mpandas\\_libs\\tslibs\\timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.Timedelta.__new__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'year' is an invalid keyword argument for __new__()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-86-f02872ffce76>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTimedelta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myear\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mpandas\\_libs\\tslibs\\timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.Timedelta.__new__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot construct a Timedelta from the passed arguments, allowed keywords are [weeks, days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds]"
     ]
    }
   ],
   "source": [
    "pd.Timedelta(year=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9256851d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "4326103f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1903', '2003', '2103', '2203', '2303', '2403', '2503', '2603']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[col for col in df_oi.columns if col[-2:]=='03']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1b183f56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x209ed289e50>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_money_x['2303'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5610f350",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e26f43ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "91348755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x209ecf8ceb0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gi5RriGx3zy+3DAIwN3zj0+mBYOEqKIuEcruG5M7fjeyAMJ3Q0R5JuYMWdpeexOEFK4IsNDsay/mtLA5br3G3j3DWEdQrzp3NwfGY72rg/YenIQQwv8s7C6q7NYTRqQSCAYV7GTEzjqTddE6BXAaiCR2nzOsAAEwl0hWBvMTtGEFARVwzsmYbJXXDMwY5PGl2QIgm9bTGlif0txX/ZXLAiiALMq9XTjTzg7QIXtpzOO0P3xJSsXpZH2a3+89UqjbOZlpCZLq3sjEwZlZRZ5vJ0NMSwlg0AZXqzzX2vd/uwOcefKXWYjB1jKypcVoEANDXFgZgpnQ6SQWLzfsRe3OYee3b85BzWATRhJ42J335MZXJNmRFkAX5BwxaE838IP13D7w4kPZ4Z3MI915/Ji5cNsvrZXWB21iJJXwqglFzbF4uRWAIYCqRe0BHLXhjcALr3jhUazGYOka2gA6oqWAxYP5PA8BkPN36N4QAUaqyWLa19yogldZGyCMZ5dBkArohkNAMu+oYAE7oZ0VQVeTJLyRv19meohJFH9UkV1m8k4GxKIIq4Zgs1k53q7lzmohpdTehrS0cwESsviq8mfpCWgRBRUlXBFaHgGg80yJQHRvH42eZrpytg5nJFJrd2TR9jWmLBDA8EbezjZz9zxbP4hhBVZHmWCFR+koVe1SDK07ux9VnzsffXH4CgPS5DbkYGI1iTmdT2j+Jkx6rungilswIopfCht2jeGXv4ZLeoy0SxGRc4+6oTFbcTecknVY+/5Tr/0Q3kHbcCmsHv2V/5vQ/mTwhY4hrVh4DwHQ7DU/EbbdTk2NGip/5J8Uwc1euCtNku4b87+xnshUQCar45ntOxlwr6OvfIpjG/K7sM5t7rH5D49PJsrqGvvHIa/jWY1tLeo/WSACGgL3z2rTvCL7yi82sGBibpGPX7tzsdFiuIXeMQAgBxbGqdjQHMa+rCZv3Z1Yh222lrTXmXz90GrZ+/TL0tZqKQG7GZOJKW7hyI+ZZEWRBWgSFziLobQ3j0xctqYRIVUH2Rc+V++xk72gU87uz903qaTFdQ+MxrayKYDymob2ptH8MOYRo0uoF86G7n8e9z+7CeI7eMMzRzz/+cis+dPfzABwWgZJeECkrfH+5eTCtdYpuiDSLADCtgi0e88BTnU3N/zlVIYQDKnrbwhiejNtN5ppDKn5784V4+pZy1Pt7w4ogC3aMoMACjvVfuBh/eUlmm+uZggyS+3ENTcU1jEwlMC+HRdDbGsKta5YDQFljBOPTybT86mJos9Ly5MIvTfR6C2oz1eW7v96O53aMAEhdC6pCOK631T6ms8m0CB559QC+//RO+3FDIC1GAAAnzunAzuEpPPV6ekWyWxFIpEUgFUFTSMXCnhY7QF0JWBFkwY4RzGC/fzFIReDHNbR3LHfGEGBmT3z8gsX4+AWLyxojGI8l7WlvxSIVgezzLt2AfuMjzNGPmd5pzjZ/25Je+/FOR8+fbY7BSzJryMmyY0wFcv0PXnS9t6UIXJvNnpYQJuMajkyb1cXOOeqVorFWuQJoChbnGprpRLJ0UPVCTmLLVkzmJKQSErpRFv97XNMRSxpoj5T2D9ISSncNyb91rt4wTGOh6YbdIsaJM0PQeUnrhshInDj7uB4ASEsDBYCE5p0+2my5LGWbCWfWUKVorFWuACJFuoZmOtIS8rMY5ismcxK0q6tLVwSyv3tHiRaBu+JbWn9sETASzRBpfbgkzg2ic3NjiMwYQWdzCBcs7cN0Uk/r45XNNSQXfjnQKRJkRVAzUsHimZsJVAyFBIv3jk2jKajaKaK5kOZvOfzv41ZxTqmuIblzk9Wg8h8yyoqAscjWC8i5QRyNpibwaXqmRQCkuhOs/vvfpI7NUkcgFcE3/+/1tPuVhBVBFhrVNdTVHMJ3PnAKzrHM2VwMWBlDfiqv5XksR+O5g+NmHxaZkVQsMqgndZNsLpirSRjTWGhWjMBNUFXw25svRFs4gKHxOAZGo9h6cAIHx2OerWS8lIN0DbnXGLcFUA1FUPkoxAxFnvxwg7mGIkEV737LvPwHIn8NgRPpBy1H47ldh0yX1LEe7XsLQf5zbhwYwwVL+9giYNIQQiBpeMcIQqqCvp4WXHlKP57YMoTzvvUUAGBxXwuWzs5sDOe1oUy5hlwxAtfC38QWQe2QCqDRLAK/CCGsGgJ/iqDFCoDJTIhCeO+dz+HYzz2CM77xK2zadwQ7hycRDijoL7GJn1QEdzy1A2PRBIIKB4uZFLohslsElvXY1xrGyFRqJvfesWnM80ie8LII8sUI5OuqkbnIq1wWiAhNQZUVQRbGpzVMxDXPi96LZceYu6TXDngPsMnFe0+bhw+ftQDDE3Fs2D2G598cwUlzO0qu5Hb+aVWF7H/u6QQXlDUqzq7BmiGgGYadVOBErgt97ZG0rKG4ZnjW1XgpgrjmrQiagilHTXNQ9d30shR4lctBU0htuKwhvwxNmH76WT535UtmtSGokmeFZT4+dNYCfOnKEwEAO4YnsWnfOFYvL72TqzO7QyXi9FEmrX4mqRtI6sKzDb18bFZbZpzK7+botQPjUBXKqMx3uoKq4RYCWBHk5I9Pn4fVddw6upYMT5rmcF+rv4BtKKDg+Flt2OzRfMvv68MBBb94eT8A4O1lUATOXZqqku0L5hhB4+L822u6yMgaWmC5QuUu3VsRZFoEXvUzv985ipVz2tHmqpB3bj6rESgGOFick89bnTiZTOTgjL42/2XvJ85px2+2Dhf9mW2RAA5NJtDfEcHyY0qf1OS0CAIK2f+sbBE0Ls4akqRhYHA8jv6OlNX74Cffil2OWgAvi9jLInCrgemEjo0Dh3H9uYsyju1yVC03VaGqGGCLgCmSQ1bVY1+r/4Dtiv52HJqM226lQpEB5wuXzSqL39RZKKQQ2cVuMbYIGha3RbB3LJq2sPe2hrFqUbfjfvpGqLslZF+nTtwGwUt7xpDQDbvq2ElzKIBPv/14AEBTsDpLNCsCpiimExqaQ2pBHUBXzMnem90P7ZEgVi/rwxevLI+lprosAlngw66hxsXZVnp0KoGJWO6EiHBATest5Dc+8PzOESgErFrU5fm8arkpKzV/wA0rAqYobnz7Emz+yjsK2pmf0N+OU+d3Fv2ZreEAJuNa2ZpwObOOVIXslsM/3bAX9z77Zlk+g5lZOF1Du0ZMF1Cu7rpA+m4/myJwu4ae3zmKlXM7MuIDEmmteg22rwSsCJiiKdQ909EUxM8/dW7Rs5vbIgG7z1A5cFoE5HANAbDbEDONhdMa3D1iFi763eWbx+avq4klzfiAl1tIImsXglUadsXBYmbG8NWrVma0+C0Fdx2C5qh6brSKcsbEmSjwq9cGAQBzOwtRBFksAofZ8Ac7PtDteSyQcg1Va+ohKwJmxnBMR2mVxG7cRT5Oi4DrRxoTp2vopT2H0RRU0e2jqaLEj/Xw/M5RKz6QXRFIi8A95KZS8NXONCzuQiHNMUGNLYLGxD2DeF6Xv6aKAHDq/E6ceay3u8cZI3h+5whOnNORc8KerGnxqkiuBHy1Mw2Lu2+8zBoCqpetwdQPG3aP4rZfbEl7rBDL8J8/eKo9BzsDhyYw4wPZrQEgtUmplmuIFQHTsLBriHHy4B/2ZTzm1V4iG171AxLh0AQJzbt+wIm8Nqs1DoWvdqZhcf+Ps2uosXFvDPo7IvjHD5zq+/VZrQEXlCc+AAC6FVxWPVpgVwIOFjMNi9v365yVUI3Wv0x94VQEX3vXSrx/1TxfLsJVC7uwfvdYzs2Ds9bgxDntecesyi6o1boMWREwDMz0vrGp1KyEKiVrMHWE0w30kbMX+n7dvdefgYNHYr6DymdnCSg7qbZFUNKnEFE3ET1BRNus35710kR0GRFtJaLtRHSr4/HbiGgfEW20fi4vRR6GKZbxaQ2aIexuj8UMUpuMa3h54LBnp0mm/ik2MNsWCWKJx1QyJ85LIl98ADCH4gDVswhK/ZhbATwphFgC4EnrfhpEpAK4A8AaACsAXE1EKxyHfEcIcar182iJ8jBMUci22nLerF7EYv61X2zBVXc8i7VWq2xmZlFIYLhQPnf5cvv2GXniA4BDEcyQOoKrANxn3b4PwLs8jjkTwHYhxE4hRALAA9brGKZuGHHNV3BOqvLL5gNHAAD3v7CnfIIxVcOdTlxOFva04OUvX4qHPnUuOppzxwcAp0UwA1xDAGYLIQ4AgPXbq4nMXAADjvt7rcckNxLRK0R0TzbXEgAQ0Q1EtJ6I1g8PF9/TnmG8GLHiA7PaTUVQqEVgGALbhyaxdHYrvnjlivwvYOoKIQTue25XRT+joymIU3w2XZTWSbUG0+RVBET0KyLa5PHjd1fvpWblf9mdABYDOBXAAQD/kO1NhBB3CSFWCSFW9fX1+fxohvHHJStm4/efvwhLLV9voRbBwFgUsaSBj77tWJw4p6MSIjIV5JnthzBexoaGpXL1WQvwiQsX45OrF1fl8/JmDQkhLs72HBENElG/EOIAEfUDGPI4bC+A+Y778wDst9570PFedwN42K/gDFNOgqqC2e0RLOptAQDMKaDRGAC8MTgJALYiYWYWu6xOowDwvtPn1VASk3BAxS2XLc9/YJkoNX10LYBrAdxu/X7I45gXASwhomMB7APwQQAfAgCpRKzj3g1gU4nyMExJ/NHJ/WgLB3DB0sKszjcGJwAgb/YIU5/sGUmNn/z2+06poSS1oVRFcDuAnxDRRwHsAfA+ACCiOQC+L4S4XAihEdGNAB4HoAK4Rwix2Xr9t4joVJiuol0A/qxEeRimJIgIq5cXPi9h68EJzOtq8l1dytQXWy2LrlEp6aoVQowAuMjj8f0ALnfcfxRARmqoEOIjpXw+w9QLbwxOYJnDGrj5py/jwJEYfvSxs2ooFeOXNw5O1FqEmsLbF4YpA2tW9mNBTyqucGQ6iUNWSipT3xyJJnFwPFZrMWoKKwKGKQM3Xbwk7b5CBC4wnhlsteI7N120BG9dnL/q92iEFQHDFMHwRBx9beGszysKYLAmmBFsPTgOAPjAGfMLzhY7WuAWi0xDs3JuO648ub+g19y1bgfe8U/rsH0ou1+ZiFgRzBC2Dk6gLRJAf5lHoc4kWBEwDc3Df34e/vVDpxX0mktXHAOFCL/Zmr3CnV1DM4c3BiexdHab7+6hRyPsGmKYAlnU24In/uJ8dOUYaq4Qu4ZmCoejCRzX21prMWoKWwQMUwS5lABgWgRF9K1jakBcMxAONvZS2NjfnmEqBM0wi+CprUPYtO9IrcWoCbGkjoiPSWRHM6wIGKYCEGZWjOArazfj7qd31lqMmsAWASsChqkICqHgSWVDEzH8ZusQJuPV74I5ndShHQW+rG2DE/jsT15GLKn7fk08aeScN9wINPa3Z5gKUUyM4MU3x3DdvS9i39h0ZYTKQSxpFDWMp9645Dvr8L9/2Iudw1P5D4aprOOa7mtI/dEMKwKGqQDFFJRFE6YlUK1hJE7imj6jYhr50H0qNc0QMAQQYdcQwzDlhoqwCKIJ053RVGVFIIQwLYIK6oFYUsf3frsD0wn/LptiPkOSNIyCXtPoFgHXETBMBSgmRiAVQbUtgoRuLpqFyutFLKnjK7/YjCtOmoO3Lem133f5Fx8DAHS3hPD+VfNzvUXR7B1LDZfRdH/fJa6Z352DxQzDlB2liBYT0wkNRKh6KmMsaS6G5bAIYkkd978wYA/qAYDx6VTwuyVUub3n8ETCvq3p/iwCWxE0eLCYLQKGqQDuYPE3HtmCU+Z34sqT52R9TTShoymoQlGq2+ogbrlH/PrVc5G0duJBNfUdEo5FOVs2z/ef3onphI4/v2iJ5/P5+PD3n08LECd9fpc4u4YAsEXAMBXBXVB2/wsD2LB7LOdrphJ6jQLF0iIoXRFolm8+oKaWlqRTEWjeiuDrj7yGf3jijZzvvevQFP53w17P557dPoIDR1IzBQq1CBo9WMwWAcNUAHfTubimIxLMvchPJ7SqB4qB1C69HElDmm0ReCuCeDJzgR70ORTmff/+OwxPxHHlKf15d/B+ayLkdw81uGuosb89w1QIZ9M53RBI6iKv7z+a0CvqQ89GOS0C6QZyuobyWQQv7hrNeGzRrY/gG49sSTFz49IAACAASURBVHtseMKc+DY0nn/ym99g8bSlCJqCjb0nZkXAMBXAGSyWu8587odoQq+pRVAW15C1AAeU1HdNaKn3jXlYBC++aSqCtkj6Ynz30296fobTBQR4ZztpPtNHp2uUqVVvNLYaZJhKQaksnFSuem5FcOLc9pr0J7ItAn9rZ07k7j+QxSKIewSLX9hlxk7y7eKDKiGpi4z5wknH62a1hTE0EU97LBe1StmtN1gRMEwFUIgAay1KBSRzLzafW3NCpcXypJwWgVz0Q9mCxR6KYOfwJAAzjiKEyJrGKsUbm0qkPT7teM/Z7REMTcR9B4vtau5wYy+F7BpimArgjBGkXEOV2XXuGJ4sKfUzVUdQjqwhyzWUNX00fYE2e/0Y1vkyd/cJLXMRNwxhv3fUVZ3srFae3W7OkX7hzVGce/uvMTAaRS5si6BCf5uZAisChqkA6TGCyqUojk4l8N47n8NND7xU9HvENWkRlC6P7RpSnBaBI0bgChZLa6mjKWg/71Qc/7thL57fOYIj00n7sWmXVeG8P6vdnDv84Ev7sO/wNLYcGM8pr60IwqwIGIYpM85eQ3Lxq0TRUndLCGcf24MnXxsq+j2koipHiwnNq6BMy54+6lYE8aSR5kr61uOv4z9/txuHJlOZQtOJ9DbdTotgVls47bl83yma0KAqlObKakQa+9szTIWQxcFCCHvxq1Q/m4W9zdBLWMSlRVDKe0iSdvpoZoyguyWUEeiVn21bBEk9TXGMTiUQTWg4NJmKC2S4hlwxAideWUpOogkdzUG1oQfXA6wIGKYiKNbCYoiURVCpGIFKVNIsATtGUJasocwYgVQEpy3owpYD454WQru0CDQ9zSJI6gJTCT3dInC5hpyZSH2t6RaB+1g30bje8G4hgBUBw1QEaREYQtgLVaWayakKlcUiKGeLCadFIBf+VYu6kNAMbD2Yakjndg394uUDaYoAMF0/I5Yi6G0NZbSydvYV6mwOpj2Xb1LZWDSB9kgw5zGNACsChqkAZFsEwt5xV8o1RFY7i2J9/KkYQemypArKCI+8cgC3rd2MmLXYn7GoCwCwce9h+3iphJbMagMArNs2bCsHiXQNKQT0dzRl7PKdqaKdzaG05/K5hvaMRrGwp9n39ztaYUXAMBVAuoa+/dhWnL+0D8/cshoLuiuz4KgON1QxlLOOIOGIETz5+iAeffWA7bZa1NOCnpYQXh5wKgLz+FPmd+C0BZ1oDqkZxWDRhI6RqTi6W8JoDqkZMQKZVvon5x6L42e1pj2XyzVkGAK7RqawqKelyG979MCKgGEqgHQNff+ZNxFUCfO6mtPcJeVEvm2xtQRyMR4Yi+Lmn76MTfuOFC2Ls+nc9qFJLJ3dlioyCyg4ZX4nXnFYBM4JYZ3NIRyZTma4hqIJHcMTCfS2htAcUjNcQ/IzP3hm5sCb7z65LWv8ZDyWRCxpoL+zqchve/TAioBhKoAzCaXSve7l/AJDCAyMRnHb2s3Y5hgMkw8Zw4glDfx0w17sGJ7Ep378B7z9H35TsCwyRqAqhG2Dk1gyuzVtsV8yqxW7DqWKvJwTwtojAVMReLiGRqbi6G0No6MpiFFXZbHd+jrLHIeBMe+iMmlZtDR4ewmAFQHDVATFoQmcOfWV/CxDCIxFE/jBc7uwJ09FrRN3wHZkMoFHXj2QNujFL9KtQwR8490r8c5T5uBwNImWkIpQQEFLOICEnqoVkFlDkYCKjqYgxqc1xF0WQVIXOHgkht7WEOZ2NeHgeCwtLpB0Nbq7+R3L8KOPnmU/T8g8/9GEVrMZ0fVISYqAiLqJ6Aki2mb97spy3D1ENEREm4p5PcPMNJx56ZXOUZcxAt0Q9mLodq/kQu7YW8MBqApl7LgLQX5uc0jFe06bh7cs6MJoNIGulpD9OJDajctgcTiomIoglvScWXDgSAw9rWHM7WyGbggMTqTSSXV7GI55Hj61+nh7XjIAJPR0V9LukSms+NLjuPfZNy2ZGrvPEFC6RXArgCeFEEsAPGnd9+IHAC4r4fUMM6Oo5rRJ2zVkAKGAeTvhs/smgLQsna7mEEZKUARSqTjjIWNTCXRbiqDFau4mm705Zwa3NwUhBHA46v35va1hO+C+Y2jSftyrdsGJsw02ANvS+dlL+wBw51GgdEVwFYD7rNv3AXiX10FCiHUAMqdP+Hw9w8w0pLumGpOvVEfNglyA3X72XKQmlAn0tIQwOpV/8Es29h+eRm9rOE0RjEaTdlpnpkUgFYFqu2jGokl40dMawlsWdCKoEp7dcch+XLqJgkr6ub7nulUA0pveAYD0FLFrKEWpV+lsIcQBALB+z6rU64noBiJaT0Trh4eHixaYYaqBtAiq0cNGWgS6QxH4HcwCpCqfdSHQ1RLEc9tHipZlYHQaC7rTs3DGphLotgq95AS2aNxSBMmUa6jJqrx2Zi3JuAUAdDeH0BIO4PSFXVj3hkMRWFlBqssiCKnm+7m7mSouVx1bBD4UARH9iog2efxcVQ0BJUKIu4QQq4QQq/r6+qr50QxTMFSCRZDUDQyOx/JWxUrsYLGRUgQFuYbsNtRAT0sYE3Etzyuys/dwFPO60uslxqYcMQKrncOUyzUUCaj2grx+d8p5INtKA6kJZuct6cNrB8YxNGH2LZKKwG0RyHPvjpe4HUjNDT6mEvChCIQQFwshVnr8PARgkIj6AcD6XWgLxFJfzzB1iVyci8kYWr9rDGf97ZP4w54xX8erDosglMc1tOvQFP7xl1ux7/C0/Zi0CIQQti+/WKYThh0HAMzd+ERcQ7flGrItAqkIkjqIzPMkezENjsdti8rpJmqzWkFcsNTcCD6zzbQKNI+paEDq3I9PJ/Gfv9tl1xO4LQJ2DZXuGloL4Frr9rUAHqry6xmmLrFdQ0VYBO1N5mI5EfO3M3dWFgetYHG2rKE3D03hu7/ejiFHF1CnRVCqIhBCwOkNk4HfLjtYbFkE8VSMIBxQQERp2Tt/dsFiAMC15yy0H5MWwYr+dvS0hLDuDdNFnHS0tXAiz/1DG/fjiw9txlartkIg3Vpi11DpiuB2AJcQ0TYAl1j3QURziOhReRAR3Q/gdwCWEdFeIvportczzEwnZREUoQisne/4tHfQ1I3c4DpdQ1IRuPsPeU1Lc7aY6GktTRHoQqTtuEctRSAVTJO12E8nnIrAlKXJIdP5S/qw6/YrcNWpc+3HpCJQFMJ5S3rx9LZDMAwB3RBQFcpI05UzonceMjOMZLsJ52zkcEBhRYASZxYLIUYAXOTx+H4AlzvuX13I6xlmpkMlBIttReDXIpCuIUPYu+KELnDDD9fj0GQcD37yXPtYr5bY0k+vG6W7hnTDpQisVNQuyzUkF2dZPxDXdHtym9NFs2JOe8ZjrQ6X03lL+vDzjfux5cA4kobhWVUslaJMF5VBY6e1NL+7ueFnEQBcWcwwFaGU9NFWa+fr1yJwxgiICEGVkNQNqApluJfcYzMPTcZtRSA8XEOFdjQVIt0HPzZlfgf5vilFYNjy2BaBtejP62qys4VaHO6igEOpnrfULBhbt20Ymi48FYE89zKYnFIEqe9UqUaAMw1WBAxTAUqxCFSF0BoO+I4RyIVXLtpBVYGmm0Hbqbj3WEc5G+FPfvCi/ZwhBHpa0ge7FNrIznTTpO6P2jECc2GXi75UBHFNt5WDHCB/omUNAKZyaI8EMhb6WW0RnNDfjnVvDEPTjTQlIXGfe/mZztRaVgQmnDfFMBWg1IKy9kgA4zF/FoFit5gw7wdVBUldoDUcwJSrU6d0Dcnd97bBVIWu4ZE1pBkChfTMM4Sw6xoAM3UUADqbQpZsBKJU/UA8adhzGprDKiJBBW9ZkN5p5oW/udjzXJy/pBf3PPsm+juaPLOz3OfeyyKY18WdRwG2CBimIlAJWUOA6R6a8KkI3G2og6qChG6gOaRiKq6luXfsITmWXMf1pXrxGyJzwlehFoHhDhZPJdAWDtjngYgQCaj2sBpnsDgcUPF/N52P689dlPaekaCKWW3ps4gB4PylfUjqAk9vO2S7x5y4A/UyLuFsWMcWgQkrAoapAKVkDcnXaT6LwuRnPb75IAAgpBKSmuka0gyR1ksontTtdE0AOK7PHOTyJ+ceC+GoTJZoxbiGnDECR8M5STiopCwCR7AYAI7tbfHdtnvVoi40BVUcmozbzfacuF1DXsHiBTydDAArAoapCHL5LLbFREBV0mbx5kIqgn99ajsAIBhQkNQNu8++M04QS+ppGUPTCQ0r+tvRElbt3f8/feBU+/nCLQKku4aiyUxFEFAcMQKj6HMUDqg4+7huAN6Fe4rLSpA9h5yuofldrAgAjhEwTEWQlb1u19Anf7wBi3pa8NeXLc/5+qBCaS6MXEi3iAyoyhhBqtOnjh7r2FjSSNuBRxM6mkMqiMgedTnL0dahkJ5Fqcrd1GM3XbTEdslIwgHVVgRJXXgGev1y3pI+PLV1GAeOxPIeKwvn5HcKW/MRGLYIGKYiJO3Zvem70jcGJ7F7JP/QGFUh/64ha+VVHYogoRt23v2k0yLQXBZBUkdTSLUXb+FoUwEUZhHoVizC6Ro6fWEX3rq4N+24cECxi9j0LDUAfrlgmdlu4pzFPZ7Pf/M9J+FC6xi3RbDCkZ3U6LAiYJgKkNC9LQKVyNcuO6gqvnfjcuGViqCzKYiX9hzGkDW8xTnAPZbU7dRRwEwnbQ6pjilnSAu8+lVG5mstiyDPwh4JpiwCrUSLYHFfK378sbPwbx8+zfP5q89cgHuvOwNAKlNJKunv/b/Ti/7cow1WBAxTAWRg0h18VRSCH49PQCXfgVq57srP+tIfrYBuGPjy2s0A0nf10YSeVq0bTehoCqr24u/O+ikkWCz1lrupmxszRmAtyiVaBABw7vG9OaeMERFCAcUeganpAgoBs9szM5EaFVYEDFMBslkEAYXsnXMuAoqSFtTMhds1dEJ/O378sbNx7vGmu0Q3BK679wX8+f0vYTKu2T17AOkaCtjpru4WEXohMQLpGsqzqphZQ1ZbiyxVweUmHFBSWUOGdwFaI8Nng2EqQNIaj+jOiFEUfzv9oFp8sBgw/d83XbQUgLm4j08ncTiawFRcS+vZ43YNCZGqgQCAX24ZzDo60o2MEeSzCEJqKmsoaZTmGvJLU1C1i9s0XSBYzVmiMwBWBAxTAeTAdLciCChkZ9fkIqAqBbiG0i0CibyvWUPtdUNgMqalZco8e8vb8ZmLl9juJbe18q3HtuLO3+zwJUe2fv9uVEsWwCzuqoZF8Lbje/Hka0PQDYGkbiBYhRGiMwk+GwxTAVbO6QAAnDy/M+1xv8HigOLvOCAVI3AvqPK+bhhQFFMhTLosgo7mINoiQUewWKQ9D5gB2VEfA+2l3vKq8nXLZSsCQ2QdOl9OTl/UhYm4hsHxmJmy6lGA1sjw2WCYCrDmpH48c8tqe5qWRFFSQdVcBApIH1WVPBaB7rAIXIpAQo6soUW9LWkDYf76f1/B73bkn2Ose9QReMqrppRcts6h5WZOp9lTaP/haWi6UdTkuKMZVgQMUyHcs3sBMwis+wkWqwUEi0nGCFypqo45BapCmIprMAQ8i6hOnd+BG1cfb7uyzrFy/6Uim4glMTQew/0v7MHguHfxlvCZPppuEVQncDvPUgT7Dk9XzQqZSbAiYJgqUlCwuICMHSDTIrBdQ8JUBIet+b+tkUxFcPrCbvzVO5bZqaWrl/fhc2uW4+/fdwoAYDyWxI7hKXzuwVftQS9u/AaLTfdYyjVUjcCtbFo3PBFHQjeK7gF1tML11QxTRXwHixX/Tefk7tq9y3VbBEesQTet4fxN3cIBFX92wWIIYebcj09rWT/HLYeaN1hsWgS6ISCEGTyuNM4hNZpuIMgxgjRYETBMFVHIv0WQbQC9G/l+mRaBtfjpZkdQWWHcGk5vNZ0LIkJbJIiJWBJJy0KRn3P9vS+gLRLEwFgUX3nnifY4yryuIatYTn6/arhpZEwgqRlWNTO7hpywImCYKqIq8GURqA4/ej5kyqc76KqqDovAsfC1+LAInLQ3BTAe06Dr6Z+zceAw5nY1YdO+cYxOJexZy3mDxZZVZFsYVXANmcPtzfYS1apdmEnw2WCYKlJIsFgzhK+ZwXJnnZE1RKk6Aqe7pq0Ai0AePz6dtC0Pp6UhM5DimpFqOpc3WKxYLhrpaqr8MmTOclaQ0AWSmoEQWwRpsCJgmCqi+NzpywCqHzfSaQu6cM5xPbjtnSemPZ6KEaQXbRVqEbRFzPnJ7hhB0kh1OI0ldVtpkc8YgXQ1VSuVM6Sacxo0w+A6AhfsGmKYKhLwqQjkLlnTBYJ51u1IUMX9N5zt+VmA1T/IoQi8soZy0RIOYGgiZmcxSQWTdFsEVkgjX7BYFsvpWWIblULGXZK6QCTIFoETVosMU0UU8mkROHbdxSLjAmaLCYciKHAYS1NIRTSup/n0hTB9/FKpxJO676ZztkUgZzZUaXcedFgEnD6aDp8NhqkiquJv2Iu9my9gHkDW97DSRwEzkNuUz8Rw0RJSEU3oadlJsthNFqdtG5q05x/kcw0FrFqKVIygWhaBgoQmkNQEVxa7YNcQw1QR1WewWLV2rCVZBI44g7zdEg7kXajdNIcCmEo4YgSKgv2HpwEAbZYi+OHvdmPPqDl5LX8dgQIhsge5K0XImuXMbagzYUXAMFXEr0Vw8QmzsLj3LHQ0FZbhk/ZZlGkRtBUxo7c5pGI6odttsV87MI7rf/AiAKApFIBCZo+ikUmzMV3erCFrNx5Leg/vqRQyRsBtqDNhRcAwVSTgaMGci/6OJvR3NJX0WWkWAaUsgkJpCQegGcIuSDtpXgeIzNkFQZUQDqiYTup2h9J8BoeUK2ZNKatesNiKEehsEbjhs8EwVcRvsLgcEJFdvCUDx8UoAhlTGJ/WAJjTvpbOagNgLq6RoLmMjEyZMQI/bagBYPO+IwCA7pZQwTIVg6wjSOgcI3DDFgHDVBG/rqHyfR7hpYExTCfM3XdbgamjQKruYDxm9ioKKEraVLRwQAWQtF09+QfTmM//zx/24ri+Fqxa2FWwTMUQUhWzxQRnDWXAioBhqojq0zVULgIK4dntqVkCLTmGvGdDWhFjVvdSVSHbzx9UFYSDrnGcPrKGAGDTvnH86XnHFhy8LpZggBBPGvZ8BiYFnw2GqSKqAl9ZQ+X7vPRFttBiMgBY1NMCANg2OAHAXMhti0AlRALp6aj5XEPObqPnuwb3VBJZWZzkwTQZsCJgmCoiLQI/PYTKwUTM9Ot3NpvZR4UWkwHA8bNaQQS8fnACRGabDLmrD6oK/uody3D9uYvs4/PFfp3FbWcs6i5YnmIJqgrimlQEvPQ54bPBMFVEdYyErCarFpoLbjGKIBJUbasgYMcGzKUjqBIuWTEbN64+3j4+Xxtq+fyinmZECixuK4VgQMH4dBKGKM4yOpopSREQUTcRPUFE26zfnlEfIrqHiIaIaJPr8duIaB8RbbR+Li9FHoapd+RGtJpxAgA4ZV4HgOKyhgBg6exWAEhzCZn3zS/U0xq2j/XTawgAju1tKUqWYpndFsH+I+aYzWLPw9FKqRbBrQCeFEIsAfCkdd+LHwC4LMtz3xFCnGr9PFqiPAxT18iFs9qKoNla+IrdCS+bbaaLuhvFOWcr9LaaaaD5Yr8Jzcwu6qpS2qhk5dx2+3YxhXVHM6UqgqsA3Gfdvg/Au7wOEkKsAzBa4mcxzIzHtgiqFCP48FkL8Lk1yyFjo37GVHqx9BhTESRdw2mcbbLfecpcAPmV3FjULDzrbq6uIjhpbod9my2CdEo9G7OFEAcAQAhxgIhmFfEeNxLRNQDWA/isEGLM6yAiugHADQCwYMGCYuVlmJpSbYvgG+8+CQDwn8/vBlDYmEonyy1FIEkNp0n1QvqbK07A+Ut7cer8zpzvJdNQq20RHNfXikhQQSxpFBUrOZrJaxEQ0a+IaJPHz1Vl+Pw7ASwGcCqAAwD+IduBQoi7hBCrhBCr+vqql3LGMOVE7syr7RpKtZgoziKY19Wc/n5qpkWgKoQLl83KWxewepn5/3vRCcXsG4tHVQgr+k33ECuCdPKeDSHExdmeI6JBIuq3rIF+AEOFfLgQYtDxXncDeLiQ1zPMTOOPV83HO1YeU1IzuWKQrpxCx1RK3Nk9n7hgMX6/cxRvXdxT8HuddVwPdt1+RVFylMpJczvwhz2HOWvIRakxgrUArrVuXwvgoUJebCkPybsBbMp2LMMcDbSGA+jvaKpaozVJJGQu5LKeoFRWzu3A+i9cnJYtNBO4cNksdLeE0Nc2s+SuNKUqgtsBXEJE2wBcYt0HEc0hIjsDiIjuB/A7AMuIaC8RfdR66ltE9CoRvQJgNYC/KFEehmE8uHTFbNx9zSrM727Of/BRzOrls7DhCxeza8hFSWdDCDEC4CKPx/cDuNxx/+osr/9IKZ/PMIw/IkEVl6yYXdJ7rF7Wh60HJ8okUe2oVm+jmQSrRYZhfHHv9WfWWgSmQnCLCYZhmAaHFQHDMEyDw4qAYRimwWFFwDAM0+CwImAYhmlwWBEwDMM0OKwIGIZhGhyq1si8ckJEwwB2V/EjewEcquLn+aVe5QLqVzaWyz/1KJOkXmWrd7kWCiEyunbOSEVQbYhovRBiVa3lcFOvcgH1KxvL5Z96lElSr7LNVLnYNcQwDNPgsCJgGIZpcFgR+OOuWguQhXqVC6hf2Vgu/9SjTJJ6lW1GysUxAoZhmAaHLQKGYZgGhxUBwzBMg8OKgGEYpsFhRWBBRCfUWgYviOizRHSpdbtuRisRUYfjdj3JVTeyOOHrqzDq9foC6k8eoPTrixUBACL6FwCPEtGiGotiQ0SXEtHjAG4BcA0AiDqI7BPR24loI4A7iejzQN3IdRUR3QfglFrL4oavL//U6/UF1O81Vo7rqyFHVRIRuS6ubgBjAC4mov8UQsRrJReAIIAvAbgAwDcBhACcQURBAFot/ymIqBXA5wF8DcALAO4jomYhxBdqJA8JIQQRrbZkSgI4h4h2CyHGaiGTUy7HQ3x9+ZOvrq4vS6a6u8YqcX01nEXgPIlEpFoPPw/gTgAfBrCklnIJIRIAHhJCnCeEeBTmH/iDQohkjf9JFQCtAAYAvCSEGADwMQAfIKLlNZDH+c/wJoB3ALgZwFkATq62PF5y8fVVkHx1dX1ZMtXdNVap66uhFAER3QjgQSL6DBHNEULoRBQCcBmAnwF4CsAHieg9RJTRmKkKcv0FEfULIV60Hg8KIX4LYCcRramWPA65PklE7wUAIYQBQADog/kPCyHETpjn7avW8VXxnbrO1zFCiF1CiANCiF8DGARwARHNrYYsWeTi68ufXHV5fVmfVXfXWCWvr4ZRBET0bgDXAvguTB/f54nodGuHtF4IcQjANgCfBvANANVa1JxynQzgb4hI+iA1IuqG2WlVr4Y8lkxtRPQ9mC6E+4goAABCiEEAWwB8xnH4rQDOIqITq7Gj9DhfXyCiUx2H/BjAUpi7NufrKvr35OurIJnq9vqy5Ku7a6zS11fDKAKYf7Q7hRBPAbgNpqn3Ceu5K4joaZiBs5/DNLXGayjXTYAZJBNCjAJoArAasE3oiiKEmADwWyHEMQAeBnCH4+mvAjiViC4norC1k3sYpu+5Gnidr087ZH8FwIsAVpIZeLzFerzSiwhfXz6p8+sLqM9rrKLX11GvCBxaeieADwGAEGI3gEcAdBLROQD+GcBzQohThRDXADgGQEXT/fLI1UJE73Qc/iMAZxJRxPrHqIZca63fnwFwNREtsWScBPAtAB+EuSv5KoDzAByokBzu+37O1/0w/cv/DbMPe9l2awXKVbXrq8TzVbHrK4dcNb2+8shWs2usVtfXUakIKBVEcWrp/wEQJaKrrPsHAPwawPkAfiyEuMXxFu8WQrxUQ7l+A2CF4yJoAvAAKmS+e8klhJgiIkUIcRDAvwH4vuOYBwD8LUzzsw/AGsusLydp16bf80Vm5sk/A3gVwMlCiJtdr6+mXNW8voo6X9Zjlby+POWqg+sLcFkZdXKNFSJT2a6vo0YRENE51u4BQgjd8bi82MdgBlQ+QUQkhDgCoAVARAhhEJEqzWIhRKzGcrUCCDsugoeEEHcLIZLVkMvtHhBC3ArgWOs1xxDRWUKI1wF8WQjxCSHEvjLKdSYR/QjAN4noJCmLQ1llO18R63zFANwkhLhCCFG2XWSRclXj+ir2fFX6+soml1LL68uSYRUR/RTAt4nobfJcOeSq+jVWpExlu76OCkVARNcCuA9mUOf91mMyACUv9iYAj8PUpHcR0RwAbwGgWcfpFTCLS5ZLylZNuawLqxVAh+NlfwfgWQDrAERc36EcMilE9GWYO8P/g1nj8ilYxTuOc5DtfCWt4zQhxFAdyVWR66tccrmOrYZcRi2uL0s2IqLbAXwPZtxhEMCNABZI2axDq3aNlUGm8lxfQogZ/wPgYgDzAFwKYI/jcdX6fZt1Et8Cs/ji6zBNvX+Tx7BcaXJ9GcBjAM6z7q8B8DqAvwcQrKBcHwVwmnW7B+ZCssrxfK3OF8tVXrlqcn1Zn3UFgC7rdj9MX3+r4/mvVvuc1YNMFTvhFf5jvgtmBeKV1n1VXkAAngHwNcexswD8F4DFrvdoZrn8yQVgBYD5FZTrCvndLdnC1v2fAPijGp4vlqsKclXq+nLJdqXr8fNgps3+DsA/wcyaarVkO76S56wuZarEya/UD8zA0c9hmo8fBzAEMzACACHr94kAjgCY7fF6heUqSK5K7YCyyuU4JgjgOQBL6+F8sVwVkauSVklO2azrfrV1+3oAPwRwbCXPWT3KZL93pd64Qn/cswHc7Lj/EZhpU2kXFkz/5L3W7TUs18ySy3rsJAA/s263ATjTuk0sF8tVDtkczx0HMxA7z7pfKcVZdzLJn7oPFhPRNUR0IRE1A9gAU0vKrIgtMFO4ZBaOAAAhxMcAXEtEYwBOSzuysAAAA0lJREFUcWcpsFx1LZdshNgDM13uOpg7ypOsbAnBcrFcpcjmwaUwE2cmgLQA7VEpkxd12X3UWqSOgekbMwDsAPCnMFO2BolIFWafjRMAdAF2hoEgooUAvgPgaQCfEkJsYrlmlFwym+UdAK4GEAfwYWFWc7JcLFfJslmvC8HMwf87APsA/LUwUzKPSpnyUklzo0jzSborlgL4kXU7AOBfADzoOuaHAN5v3e6zfnfCMj9Zrhkp12zr97kAPsBysVwVkK3X+n0yXAHbo1EmPz91YxFYpuRXAahE9CiAdliVjkIIjYg+DWA/EV0gzI6JADAJ4E0yC6PeQ0SXCyH2wOxlznLNXLnWCCGeLZdMLNfRI1eZZXsFQFkslHqUqSCqrXmyaNELALwMs6f2n8KMql8GYA8cu1WYTZaekloVZoXfbpipVn0sF8vFch29ctWrbPUoU8HfoZYf7jhB5wH4iOP+v1kn7ToAG6zHFJh+t58AWAhgsXUCT2O5WC6W6+iXq15lq0eZCv4OtRbAOknNAMJI+c4+DOCb1u2NAP7cur0KwAMsF8vFcjWeXPUqWz3KVOhPXaSPCiGiQoi4SPU8uQTAsHX7egAnENHDMEuvNwDVmVbEcrFcLFf9yFWvstWjTIVSN8FiwM6tFQBmI9WvfAJmOfZKAG8KqxOhsFQsy8VysVyNJVe9ylaPMvmlLiwCBwbMkvRDAE62tOgXARhCiGdEmdvRslwsF8s1I+WqV9nqUSZ/1No35f6BWYZtwGyG9tFay8NysVwsV/3JVa+y1aNMfn7IEr5uIKJ5MHtw/KMQIl5reSQsV2GwXIXBchVOPcpWjzL5oe4UAcMwDFNd6i1GwDAMw1QZVgQMwzANDisChmGYBocVAcMwTIPDioBhGKbBYUXAMGWCiDqJ6JO1loNhCoUVAcOUj04ArAiYGQcrAoYpH7cDWExEG4no27UWhmH8wgVlDFMmiGgRgIeFECtrLArDFARbBAzDMA0OKwKGYZgGhxUBw5SPCQBttRaCYQqFFQHDlAkhxAiAZ4loEweLmZkEB4sZhmEaHLYIGIZhGhxWBAzDMA0OKwKGYZgGhxUBwzBMg8OKgGEYpsFhRcAwDNPgsCJgGIZpcP4/JbJtNZ/tEEsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_close_x['2403'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f192b484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2022-02-11    521.9\n",
       "2022-02-15    532.8\n",
       "2022-03-09    635.3\n",
       "2022-03-25    597.0\n",
       "2022-04-08    600.3\n",
       "              ...  \n",
       "2023-01-10    521.2\n",
       "2023-01-11    519.6\n",
       "2023-01-12    525.9\n",
       "2023-01-13    565.0\n",
       "2023-01-16    532.0\n",
       "Name: 2302, Length: 136, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_close['2302'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1b26207a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2021-03-01    420.0\n",
       "2021-03-02    420.0\n",
       "2021-03-03    405.0\n",
       "2021-03-04    414.5\n",
       "2021-03-05    432.1\n",
       "              ...  \n",
       "2023-12-25    556.0\n",
       "2023-12-26    561.3\n",
       "2023-12-27    577.0\n",
       "2023-12-28    566.9\n",
       "2023-12-29    546.2\n",
       "Name: 2403, Length: 692, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_close['2403'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2866bfc6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bef38758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1709'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "previous_year_month(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb989e05",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc67f92e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1001\n",
      "1209\n",
      "1001\n",
      "1805\n",
      "1001\n",
      "1001\n",
      "1404\n",
      "2103\n",
      "1402\n",
      "1001\n",
      "1001\n",
      "1912\n",
      "1503\n",
      "1001\n",
      "1801\n",
      "2004\n",
      "1906\n",
      "1001\n",
      "1404\n",
      "1303\n",
      "1003\n",
      "1407\n",
      "1403\n",
      "1505\n",
      "1005\n",
      "1505\n",
      "2208\n",
      "1109\n",
      "1403\n",
      "1307\n",
      "1403\n",
      "1001\n",
      "2109\n",
      "1411\n",
      "2101\n",
      "1001\n",
      "1506\n",
      "1203\n",
      "1507\n",
      "2002\n",
      "1307\n",
      "1001\n",
      "1109\n",
      "2105\n",
      "2011\n",
      "2110\n",
      "1301\n",
      "1405\n",
      "1001\n",
      "1307\n",
      "1305\n",
      "1001\n",
      "2001\n",
      "1307\n",
      "1001\n",
      "2005\n",
      "1809\n",
      "1411\n",
      "1411\n",
      "1507\n",
      "1906\n",
      "1001\n",
      "2002\n",
      "1509\n",
      "1001\n",
      "1312\n",
      "1312\n",
      "1812\n",
      "2001\n",
      "1001\n",
      "1307\n",
      "1001\n",
      "1001\n",
      "1001\n",
      "1001\n",
      "1605\n",
      "1001\n"
     ]
    }
   ],
   "source": [
    "for 期货前缀 in 期货前缀ss:\n",
    "    df = get_df(期货前缀)\n",
    "    df_open = df.pivot(index='t', columns='code', values='open')\n",
    "    df_high = df.pivot(index='t', columns='code', values='high')\n",
    "    df_low = df.pivot(index='t', columns='code', values='low')\n",
    "    df_close = df.pivot(index='t', columns='code', values='close')\n",
    "    df_oi = df.pivot(index='t', columns='code', values='oi')\n",
    "    df_money = df.pivot(index='t', columns='code', values='amount')\n",
    "    print(df_close.columns[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d07df931",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>code</th>\n",
       "      <th>1303</th>\n",
       "      <th>1304</th>\n",
       "      <th>1305</th>\n",
       "      <th>1306</th>\n",
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       "    <tr>\n",
       "      <th>2012-12-03</th>\n",
       "      <td>1339.0</td>\n",
       "      <td>1333.0</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>1323.0</td>\n",
       "      <td>1330.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2012-12-04</th>\n",
       "      <td>1328.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-05</th>\n",
       "      <td>1341.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>1333.0</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>1334.0</td>\n",
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       "      <td>1326.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2012-12-06</th>\n",
       "      <td>1337.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2012-12-07</th>\n",
       "      <td>1342.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>1330.0</td>\n",
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       "      <td>1330.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1835.0</td>\n",
       "      <td>1876.0</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1864.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>1822.0</td>\n",
       "      <td>1793.0</td>\n",
       "      <td>1786.0</td>\n",
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       "    <tr>\n",
       "      <th>2023-12-26</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1833.0</td>\n",
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       "      <th>2023-12-27</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>1892.0</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>1854.0</td>\n",
       "      <td>1855.0</td>\n",
       "      <td>1834.0</td>\n",
       "      <td>1823.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1790.0</td>\n",
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       "    <tr>\n",
       "      <th>2023-12-28</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>1894.0</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>1882.0</td>\n",
       "      <td>1858.0</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>1835.0</td>\n",
       "      <td>1832.0</td>\n",
       "      <td>1801.0</td>\n",
       "      <td>1790.0</td>\n",
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       "    <tr>\n",
       "      <th>2023-12-29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1867.0</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>1901.0</td>\n",
       "      <td>1879.0</td>\n",
       "      <td>1883.0</td>\n",
       "      <td>1853.0</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>1815.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2693 rows × 142 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "code          1303    1304    1305    1306    1307    1308    1309    1310  \\\n",
       "t                                                                            \n",
       "2012-12-03  1339.0  1333.0  1318.0  1323.0  1330.0  1334.0  1330.0  1336.0   \n",
       "2012-12-04  1328.0  1321.0  1311.0  1307.0  1312.0  1310.0  1313.0  1309.0   \n",
       "2012-12-05  1341.0  1332.0  1331.0  1333.0  1326.0  1338.0  1334.0  1331.0   \n",
       "2012-12-06  1337.0  1327.0  1325.0  1325.0  1326.0     NaN  1328.0  1326.0   \n",
       "2012-12-07  1342.0  1332.0  1331.0  1330.0  1330.0     NaN  1333.0  1330.0   \n",
       "...            ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "2023-12-25     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2023-12-26     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2023-12-27     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2023-12-28     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2023-12-29     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "code          1311  1312  ...    2403    2404    2405    2406    2407    2408  \\\n",
       "t                         ...                                                   \n",
       "2012-12-03     NaN   NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2012-12-04     NaN   NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2012-12-05  1326.0   NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2012-12-06  1324.0   NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2012-12-07  1319.0   NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "...            ...   ...  ...     ...     ...     ...     ...     ...     ...   \n",
       "2023-12-25     NaN   NaN  ...  1835.0  1876.0  1872.0  1864.0  1841.0  1847.0   \n",
       "2023-12-26     NaN   NaN  ...  1833.0  1879.0  1875.0  1863.0  1844.0  1843.0   \n",
       "2023-12-27     NaN   NaN  ...  1842.0  1892.0  1888.0  1880.0  1854.0  1855.0   \n",
       "2023-12-28     NaN   NaN  ...  1845.0  1894.0  1891.0  1882.0  1858.0  1856.0   \n",
       "2023-12-29     NaN   NaN  ...  1867.0  1918.0  1913.0  1901.0  1879.0  1883.0   \n",
       "\n",
       "code          2409    2410    2411    2412  \n",
       "t                                           \n",
       "2012-12-03     NaN     NaN     NaN     NaN  \n",
       "2012-12-04     NaN     NaN     NaN     NaN  \n",
       "2012-12-05     NaN     NaN     NaN     NaN  \n",
       "2012-12-06     NaN     NaN     NaN     NaN  \n",
       "2012-12-07     NaN     NaN     NaN     NaN  \n",
       "...            ...     ...     ...     ...  \n",
       "2023-12-25  1827.0  1822.0  1793.0  1786.0  \n",
       "2023-12-26  1824.0  1827.0  1790.0  1801.0  \n",
       "2023-12-27  1834.0  1823.0  1800.0  1790.0  \n",
       "2023-12-28  1835.0  1832.0  1801.0  1790.0  \n",
       "2023-12-29  1853.0  1842.0  1815.0  1800.0  \n",
       "\n",
       "[2693 rows x 142 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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    }
   ],
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    "df_close.columns[0]"
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b47b7c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1909-01-01 00:00:00')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = '1909'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "005d6607",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1cecca9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2018-03-26    405.1\n",
       "2018-03-27    402.8\n",
       "2018-03-28    385.5\n",
       "2018-03-29    383.2\n",
       "2018-03-30    392.4\n",
       "              ...  \n",
       "2023-12-25      NaN\n",
       "2023-12-26      NaN\n",
       "2023-12-27      NaN\n",
       "2023-12-28      NaN\n",
       "2023-12-29      NaN\n",
       "Name: 1909, Length: 1403, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = '1909'\n",
    "col_x = \n",
    "df_close[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "428e7424",
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
   "id": "5e70726a",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c286f4ce",
   "metadata": {},
   "outputs": [],
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